The design of embedded computer systems for modern wireless communication devices finds itself under increasing technological and commercial pressures. This design crisis is fueled by an unrelenting growth in algorithmic complexity, which by far outpaces the growth in design productivity, thus making it increasingly difficult to design entire embed- ded systems. On the other hand, the commercial reality in the wireless communications sector dictates ever shortening design cycles to achieve quicker time to market. This thesis examines the traditional design process of embedded systems for wireless communications, identifies the key bottlenecks which inhibit increased design productiv- ity, and proposes the Open Tool Integration Environment (OTIE) as an effective means of removing these bottlenecks. A flexible, scalable, robust, and secure implementation of OTIE is presented, based on a Single System Description (SSD), providing a single, central repository for all refinement information ...

The evolution of electronic devices has made a tremendous progress within the last 50 years, thus today's world they can be found nearly everywhere, such as cell phones, camcorders, antiblock-brakes. The design of such complex system, that consists of hardware and software has to cope with several obstacles like for example high system complexity and increasing economical demands like shortened time-to-market. Those barriers get especially visible in the wireless domain. Here, design productivity lacks behind the possible computational complexity famously described with Moore's law. The importance to cope efficently with these problems of system designing has been highlighted by the International Technology Roadmap for Semiconductors. This thesis examines one of the design tasks namely design space exploration. Since the description of systems raises constantly its level of abstraction which causes a higher ability for exploring design variants the automatic derivation ...

Indoor location has experienced a major boost in recent years. location based services (LBS), which until recently were restricted to outdoor scenarios and the use of GPS, have also been extended into buildings. From large public structures such as airports or hospitals to a multitude of industrial scenarios, LBS has become increasingly present in indoor scenarios. Of the various technologies that can be used to achieve this indoor location, the ones based on ultra- wideband (UWB) signals have become ones of the most demanded due primarily to their accuracy in position estimation. Additionally, the appearance in the market of more and more manufacturers and products has lowered the prices of these devices to levels that allow to think about their use for large deployments with a contained budget. By their nature, UWB signals are very resistant to the multi-path phenomenon, ...

Electroencephalography (EEG) is a non-invasive neurorecording technique, which has the potential to be used for 24/7 neuromonitoring in daily life, e.g., in the context of neural prostheses, brain-computer interfaces, or for improved diagnosis of brain disorders. Although existing mobile wireless EEG headsets are a useful tool for short-term experiments, they are still too heavy, bulky and obtrusive, for long-term EEG-monitoring in daily life. However, we are now witnessing a wave of new miniature EEG sensor devices containing small electrodes embedded in them, which we refer to as Mini-EEGs. Mini-EEGs ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. However, due to their miniaturization, these mini-EEGs have the drawback that only a few EEG channels can be recorded within a small area. The latter also implies that the ...

eXtended Reality (XR) technology, also called Mixed Reality (MR), is in constant development and improvement in terms of hardware and software to offer relevant experiences to users. One of the advances in XR has been the introduction of real visual information in the virtual environment, offering a more natural interaction with the scene and a greater acceptance of technology. Another advance has been achieved with the representation of the scene through a video that covers the entire environment, called 360-degree or omnidirectional video. These videos are acquired by cameras with omnidirectional lenses that cover the 360-degrees of the scene and are generally viewed by users through a head-tracked Head Mounted Display (HMD). Users only visualize a subset of the 360-degree scene, called viewport, which changes with the variations of the viewing direction of the users, determined by the movements of ...

Error control coding is an essential part of modern communications systems. LDPC codes have been demonstrated to offer performance near the fundamental limits of channels corrupted by random noise. Optimal maximum likelihood decoding of LDPC codes is too complex to be practically useful even at short block lengths and so a graph-based message passing decoder known as the belief propagation algorithm is used instead. In fact, on graphs without closed paths known as cycles the iterative message passing decoding is known to be optimal and may converge in a single iteration, although identifying the message update schedule which allows single-iteration convergence is not trivial. At finite block lengths graphs without cycles have poor minimum distance properties and perform poorly even under optimal decoding. LDPC codes with large block length have been demonstrated to offer performance close to that predicted for ...

Humans without larynx need to use a substitution voice to re-obtain speech. The electro-larynx (EL) is a widely used device but is known for its unnatural and monotonic speech quality. Previous research tackled these problems, but until now no significant improvements could be reported. The EL speech system is a complex system including hardware (artificial excitation source or sound transducer) and software (control and generation of the artificial excitation signal). It is not enough to consider one separated problem, but all aspects of the EL speech system need to be taken into account. In this thesis we would like to push forward the boundaries of the conventional EL device towards a new bionic electro-larynx speech system. We formulate two overall scenarios: a closed-loop scenario, where EL speech is excited and simultaneously recorded using an EL speech system, and the artificial ...

The explosion of Deep Learning (DL) added a boost to the already rapidly developing field of Computer Vision to such a point that vision-based tasks are now parts of our everyday lives. Applications such as image classification, photo stylization, or face recognition are nowadays pervasive, as evidenced by the advent of modern systems trivially integrated into mobile applications. In this thesis, we investigated and enhanced the visual counting task, which automatically estimates the number of objects in still images or video frames. Recently, due to the growing interest in it, several Convolutional Neural Network (CNN)-based solutions have been suggested by the scientific community. These artificial neural networks, inspired by the organization of the animal visual cortex, provide a way to automatically learn effective representations from raw visual data and can be successfully employed to address typical challenges characterizing this task, ...

Due to significant advances made over the last few decades in the areas of (wireless) networking, communications and microprocessor fabrication, the use of sensor networks to observe physical phenomena is rapidly becoming commonplace. Over this period, many aspects of sensor networks have been explored, yet a thorough understanding of how to analyse and process the vast amounts of sensor data collected remains an open area of research. This work, therefore, aims to provide theoretical, as well as practical, advances this area. In particular, we consider the problem of inferring certain underlying properties of the monitored phenomena, from our sensor measurements. Within mathematics, this is commonly formulated as an inverse problem; whereas in signal processing, it appears as a (multidimensional) sampling and reconstruction problem. Indeed it is well known that inverse problems are notoriously ill-posed and very demanding to solve; meanwhile ...

The thriving development of wireless communications calls for innovative and advanced signal processing techniques targeting at an enhanced performance in terms of reliability, throughput, robustness, efficiency, flexibility, etc.. This thesis addresses such a compelling demand and presents new and intriguing progress towards fulfilling it. We mainly concentrate on two advanced multi-dimensional signal processing challenges for wireless systems that have attracted tremendous research attention in recent years, multi-carrier Multiple-Input Multiple-Output (MIMO) systems and multi-dimensional harmonic retrieval. As the key technologies of wireless communications, the numerous benefits of MIMO and multi-carrier modulation, e.g., boosting the data rate and improving the link reliability, have long been identified and have ignited great research interest. In particular, the Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user MIMO downlink with Space-Division Multiple Access (SDMA) combines the twofold advantages of MIMO and multi-carrier modulation. It is the essential element ...

Multi-antenna processing is widely adopted as one of the key enabling technologies for current and future cellular networks. Particularly, multiuser downlink beamforming (also known as space-division multiple access), in which multiple users are simultaneously served with spatial transmit beams in the same time and frequency resource, achieves high spectral efficiency with reduced energy consumption. To harvest the potential of multiuser downlink beamforming in practical systems, optimal beamformer design shall be carried out jointly with network resource allocation. Due to the specifications of cellular standards and/or implementation constraints, resource allocation in practice naturally necessitates discrete decision makings, e.g., base station (BS) association, user scheduling and admission control, adaptive modulation and coding, and codebook-based beamforming (precoding). This dissertation focuses on the joint optimization of multiuser downlink beamforming and discrete resource allocation in modern cellular networks. The problems studied in this thesis involve ...

The goal of this Ph.D. dissertation is to address a number of challenges encountered in the digital baseband design of modern and future wireless communication systems. The fast and continuous evolution of wireless communications has been driven by the ambitious goal of providing ubiquitous services that could guarantee high throughput, reliability of the communication link and satisfy the increasing demand for efficient re-utilization of the heavily populated wireless spectrum. To cope with these ever-growing performance requirements, researchers around the world have introduced sophisticated broadband physical (PHY)-layer communication schemes able to accommodate higher bandwidth, which indicatively include multiple antennas at the transmitter and receiver and are capable of delivering improved spectral efficiency by applying interference management policies. The merging of Multiple Input Multiple Output (MIMO) schemes with the Orthogonal Frequency Division Multiplexing (OFDM) offers a flexible signal processing substrate to implement ...

Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their ...

he main objective of the Magnetospheric Multiscale (MMS) mission is to characterize fine-scale structures in the Earth’s magnetotail and magnetopause. These dynamic structures traverse the MMS spacecraft formation at high speed and generate magnetic field signatures that cross the sensitive frequency bands of both search-coil and fluxgate magnetometers. An improved understanding of these events is only possible by combining data from both instrument types for magnetospheric event analysis. This combination is done using a model-based sensor fusion approach that merges data from both instrument types to a virtual instrument with flat gain curve, linear phase and known timing properties as well as the highest sensitivity and lowest noise floor. The generation of the underlying instrument models requires precise knowledge of the instrument frequency responses and timing. This knowledge was obtained in a dedicated end-to-end measurement campaign using a purpose-built magnetic ...

Future wireless networks will evolve to integrate communication, localization, and sensing capabilities. This evolution is driven by emerging application platforms such as digital twins, on the one hand, and advancements in wireless technologies, on the other, characterized by increased bandwidths, more antennas, and enhanced computational power. Crucial to this development is the application of artificial intelligence (AI), which is set to harness the vast amounts of available data in the sixth-generation (6G) of mobile networks and beyond. Integrating AI and machine learning (ML) algorithms, in particular, with wireless localization offers substantial opportunities to refine communication systems, improve the ability of wireless networks to locate the users precisely, enable context-aware transmission, and utilize processing and energy resources more efficiently. In this dissertation, advanced ML algorithms for enhanced wireless localization are proposed. Motivated by the capabilities of deep neural networks (DNNs) and ...