80 research outputs found

    Antennas and Propagation

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    This Special Issue gathers topics of utmost interest in the field of antennas and propagation, such as: new directions and challenges in antenna design and propagation; innovative antenna technologies for space applications; metamaterial, metasurface and other periodic structures; antennas for 5G; electromagnetic field measurements and remote sensing applications

    Smart Sensor Technologies for IoT

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    The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

    Voice inactivity ranking for enhancement of speech on microphone arrays

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    Motivated by the problem of improving the performance of speech enhancement algorithms in non-stationary acoustic environments with low SNR, a framework is proposed for identifying signal frames of noisy speech that are unlikely to contain voice activity. Such voice-inactive frames can then be incorporated into an adaptation strategy to improve the performance of existing speech enhancement algorithms. This adaptive approach is applicable to single-channel as well as multi-channel algorithms for noisy speech. In both cases, the adaptive versions of the enhancement algorithms are observed to improve SNR levels by 20dB, as indicated by PESQ and WER criteria. In advanced speech enhancement algorithms, it is often of interest to identify some regions of the signal that have a high likelihood of being noise only i.e. no speech present. This is in contrast to advanced speech recognition, speaker recognition, and pitch tracking algorithms in which we are interested in identifying all regions that have a high likelihood of containing speech, as well as regions that have a high likelihood of not containing speech. In other terms, this would mean minimizing the false positive and false negative rates, respectively. In the context of speech enhancement, the identification of some speech-absent regions prompts the minimization of false positives while setting an acceptable tolerance on false negatives, as determined by the performance of the enhancement algorithm. Typically, Voice Activity Detectors (VADs) are used for identifying speech absent regions for the application of speech enhancement. In recent years a myriad of Deep Neural Network (DNN) based approaches have been proposed to improve the performance of VADs at low SNR levels by training on combinations of speech and noise. Training on such an exhaustive dataset is combinatorically explosive. For this dissertation, we propose a voice inactivity ranking framework, where the identification of voice-inactive frames is performed using a machine learning (ML) approach that only uses clean speech utterances for training and is robust to high levels of noise. In the proposed framework, input frames of noisy speech are ranked by ‘voice inactivity score’ to acquire definitely speech inactive (DSI) frame-sequences. These DSI regions serve as a noise estimate and are adaptively used by the underlying speech enhancement algorithm to enhance speech from a speech mixture. The proposed voice-inactivity ranking framework was used to perform speech enhancement in single-channel and multi-channel systems. In the context of microphone arrays, the proposed framework was used to determine parameters for spatial filtering using adaptive beamformers. We achieved an average Word Error Rate (WER) improvement of 50% at SNR levels below 0dB compared to the noisy signal, which is 7±2.5% more than the framework where state-of-the-art VAD decision was used for spatial filtering. For monaural signals, we propose a multi-frame multiband spectral-subtraction (MF-MBSS) speech enhancement system utilizing the voice inactivity framework to compute and update the noise statistics on overlapping frequency bands. The proposed MF-MBSS not only achieved an average PESQ improvement of 16% with a maximum improvement of 56% when compared to the state-of-the-art Spectral Subtraction but also a 5 ± 1.5% improvement in the Word Error Rate (WER) of the spatially filtered output signal, in non-stationary acoustic environments

    Inverse Problem Formulation and Deep Learning Methods for Ultrasound Beamforming and Image Reconstruction

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    Ultrasound imaging is among the most common medical imaging modalities, which has the advantages of being real-time, non-invasive, cost-effective, and portable. Medical ultrasound images, however, have low values of signal-to-noise ratio due to many factors, and there has been a long-standing line of research on improving the quality of ultrasound images. Ultrasound transducers are made from piezoelectric elements, which are responsible for the insonification of the medium with non-invasive acoustic waves and also the reception of backscattered signals. Design optimizations span all steps of the image formation pipeline, including system architecture, hardware development, and software algorithms. Each step entails parameter optimizations and trade-offs in order to achieve a balance in competing effects such as cost, performance, and efficiency. The current thesis is devoted to research on image reconstruction techniques in order to push forward the classical limitations. It is tried not to be restricted into a specific class of computational imaging or machine learning method. As such, classical approaches and recent methods based on deep learning are adapted according to the requirements and limitations of the image reconstruction problem. In other words, we aim to reconstruct a high-quality spatial map of the medium echogenicity from raw channel data received from piezoelectric elements. All other steps of the ultrasound image formation pipeline are considered fixed, and the goal is to extract the best possible image quality (in terms of resolution, contrast, speckle pattern, etc.) from echo traces acquired by transducer elements. Two novel approaches are proposed on super-resolution ultrasound imaging by training deep models that create mapping functions from observations recorded from a single transmission to high-quality images. These models are mainly developed to resolve the necessity of several transmissions, which can potentially be used in applications that require both high framerate and image quality. The remaining four contributions are on beamforming, which is an essential step in medical ultrasound image reconstruction. Different approaches, including independent component analysis, deep learning, and inverse problem formulations, are utilized to tackle the ill-posed inverse problem of receive beamforming. The primary goal of novel beamformers is to find a solution to the trade-off between image quality and framerate. The final chapter consists of concluding remarks on each of our contributions, where the strengths and weaknesses of our proposed techniques based on classical computational imaging and deep learning methods are outlined. There is still a large room for improvement in all of our proposed techniques, and the thesis is concluded by providing avenues for future research to attain those improvements

    Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

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    The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

    Hidden Markov Models

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    Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

    Advances in Data Mining Knowledge Discovery and Applications

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    Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

    Sensor Signal and Information Processing II

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    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

    Recent Advances in Signal Processing

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    The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

    Development of a K-band FMCW flexible radar prototype for detection and classification of nano-drones

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    © Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerNano-drones of the size of an insect can be used to perform stealthy surveillance or to gather intelligence crucial to attack roles at a relatively short range and within enclosed spaces and buildings. Conventional radar systems have been optimised to detect and classify bigger targets and are not specifically designed to detect nano-targets of less than 5 cm in size. Hence, this project aims to develop a radar system to detect and classify an insect-like size drone that corresponds to a low RCS. This will exhibit challenges due to the nature of the weak echoed signal that will be masked by an uninterested target with a stronger echoed signal. To tackle this sort of problem, micro Doppler extraction is applied for better target detection. This type of target that consists of a bladed propeller will give rise to a significant micro-Doppler signature that will contribute to the discernment of the interested target. An ad-hoc S-band FMCW radar prototype using off-the-shelf components An ad-hoc S-band FMCW radar prototype using off-the-shelf components has been successfully delivered. This prototype act as a groundwork for the next research phase of design and development for a higher frequency. Then, with the strong foundation of the S-band demonstrator, a flexible K-band FMCW radar prototype has successfully delivered aiming to meet the research purpose. The radar prototype offers a wide range of flexibility for the user to select the radar parameters (like operating frequency, ramp duration, bandwidth and integration time) and configure its performance. It will collect the signatures of real targets (nano-drone model) so that their performance can be assessed on experimental data. The results demonstrated that a nano-drone, a small size of less than 5 cm can be detected with the radar prototype developed.Ph
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