Emulation of Narrowband Powerline Data Transmission Channels and Evaluation of PLC Systems

This work proposes advanced emulation of the physical layer behavior of NB-PLC channels and the application of a channel emulator for the evaluation of NB-PLC systems. In addition, test procedures and reference channels are proposed to improve efficiency and accuracy in the system evaluation and classification. This work shows that the channel emulator-based solution opens new ways toward flexible, reliable and technology-independent performance assessment of PLC modems

High efficiency wide-band line drivers in low voltage CMOS using Class-D techniques

In this thesis, the applicability of Class-D amplifiers to integrated wide-band communication line driver applications is studied. While Class-D techniques can address some of the efficiency limitations of linear amplifier structures and have shown promising results in low frequency applications, the low frequency techniques and knowledge need further development in order to improve their practicality for wide band systems. New structures and techniques to extend the application of Class-D to wide-band communication systems, in particular the HomePlug AV wire- line communication standard, will be proposed. Additionally, the digital processing requirements of these wide-band systems drives rapid movement towards nanometer technology nodes and presents new challenges which will be addressed, and new opportunities which will be exploited, for wide-band integrated Class-D line drivers. There are three main contributions of this research. First, a model of Class-D efficiency degradation mechanisms is created, which allows the impact of high-level design choices such as supply voltage, process technology and operating frequency to be assessed. The outcome of this section is a strategy for pushing the high efficiency of Class-D to wide band communication applications, with switching frequencies up to many hundreds of Megahertz. A second part of this research considers the design of efficient, fast and high power Class-D output stages, as these are the major efficiency and bandwidth bottleneck in wide-band applications. A novel NMOS-only totem pole output stage with a fast, integrated drive structure will be proposed. In a third section, a complete wide-band Class-D line driver is designed in a 0.13μm digital CMOS process. The line driver is systematically designed using a rigorous development methodology and the aims are to maximise the achievable signal bandwidth while minimising power dissipation. Novel circuits and circuit structures are proposed as part of this section and the resulting fabricated Class-D line driver test chip shows an efficiency of 15% while driving a 30MHz wide signal with an MTPR of 22dB, at 33mW injected power

On Application of Wireless Sensor Networks for Healthcare Monitoring

With the recent advances in embedded systems and very low power ,wireless tech­ nologies, there has been a great interest in the development and application of a new class of distributed Wireless body area network for health monitoring. The first part of the thesis presents a remote patient monitoring system within the scope of Body Area Network standardization. In this regime, wireless sensor networks are used to continuously acquire the patient’s Electrocardiogram signs and transmit data to the base station via IEEE.802.15. The personal Server (PS) which is responsible to provide real-time displaying, storing, and analyzing the patient’s vital signs is developed in MATLAB. It also transfers ECG streams in real-time to a remote client such as a physician or medical center through internet. The PS has the potential to be integrated with home or hospital computer systems. A prototype of this system has been developed and implemented. Tlie developed system takes advantage of two important features for healthcare monitoring: (i) ECG data acqui­ sition using wearable sensors and (ii) real-time data remote through internet. The fact that our system is interacting with sensor network nodes using MATLAB makes it distinct from other previous works. The second part is devoted to the study of indoor body-area channel model for 2.4 GHz narrowband communications. To un­ derstand the narrowband radio propagation near the body, several measurements are carried out in two separate environments for different on body locations. On the basis of these measurements, we have characterized the fading statistics on body links and we have provided a physical interpretation of our results

Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines

Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%

Design und Evaluation von Hardware-Architekturen zur Powerline-basierten Kommunikation unter extremen Umweltbedingungen

Moderne elektronische Geräte setzten vermehrt auf den Austausch von Informationen mit dem Benutzer oder einem Online-Dienst des Herstellers, wie zum Beispiel im Smart Home, der Staubsauger-Roboter oder die Waschmaschine. Die Verbindung erfolgt zumeist über drahtlose Kommunikation zum Beispiel auf dem 2,4 GHz-Funkkanal, welcher gerade in dicht besiedelten Städten teilweise schon überlastet ist. Eine Alternative bietet die drahtgebundene Kommunikation über die Stromversorgungsleitung, auch Powerline-Kommunikation genannt. Hierbei werden die Informationen auf freie Frequenzbänder oberhalb der Netzfrequenz moduliert und konkurrieren dabei nur mit den anderen Teilnehmern des eigenen Stromnetzes. Ein Problem bei dieser Art der Kommunikation ist der Übetragungskanal, der nicht für eine hochfrequente Übertragung ausgelegt ist und starke Störungen durch Lastwechsel oder Reflexionen an Impedanzsprüngen erzeugt. Um diese Störungen zu kompensieren verwenden moderne Powerline-Kommunikationsstandards robuste Kanalkodierverfahren, um Fehler in den übertragenen Informationen empfangsseitig korrigieren zu können. Auf Grund dieser robusten Fehlerkorrketurverfahren, eignen sich diese Standards auch für die Kommunikation unter extremen Umweltbedingungen in der Tiefenbohrtechnik. Dabei befinden sich die elektronischen Komponenten entlang der letzten 100 m des Bohrstrangs mehrere Kilometer tief unter der Erde, wo Umgebungstemperaturen mehr als 150 °C, Drücke bis 207 MPa und mechanische Schocks auftreten. Diese extremen Umweltbedingungen haben sowohl Einfluss auf die elektronischen Komponenten, als auch auf den Übetragungskanal der Powerline-Kommunikation selbst. In dieser Arbeit wird erstmalig eine Entwurfsraumexploration für hochtempteraturfeste Hardware-Plattformen eines Breitband-Powerline-Kommunikationssystems durchgeführt. Dabei wird ein Abtausch zwischen Durchsatz, Flexibiltät und Hardware-Ressourcen aufgezeigt, der verschiedene Pareto-optimale Punkte enthält. Diese Pareto-optimalen Punkte umfassen sowohl Prozessor-basiere Plattformen, als auch eine dedizierte Implementierung. Die dedizierte Implementierung wird anschließend in einer FPGA-basierten Emulationen bezüglich Durchsatz, Latenz und Skalierbarkeit des Netzwerkes evaluiert und optimale Konfigurationen aufgezeigt. Abschließend wird diese optimale Konfiguration für die Fertigung als Chip in einer Hochtemperaturtechnologie vorbereitet. Der gefertigte Chip wird auf einer hochtemperaturfesten Leiterplatte in einer Klimakammer unter extremen Umweltbedingungen verifiziert und evaluiert. Die Ergebnisse zeigen eine geringe Leistungsaufnahme und eine stabile Kommunikation mit geringen Paketverlustraten bis zu einer Sperrschichttemperatur von 220 °C. Die Messungen zeigen einen linearen Abtausch zwischen Spannungsversorgung und Stabilität der Kommunikation von 7 mW/°C. Damit erweitert diese Arbeit den aktuellen Stand der Forschung um den ersten Breitband-Powerline-Kommunikation-Chip, der unter extremen Umweltbedingungen evaluiert und charakterisiert wurde

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Modelling and analysis of next generation home networks

As Home Networking grows over the next 20 years the need for accurate models for both the network and the hardware becomes apparent. In this work, these two areas are considered together to develop a combined hardware and network model for a HomePlug power line based network. This change of focus is important when the type of devices that will be running on tomorrow's home network is considered. It will have evolved from a simple network of PCs sharing an Internet connection to a large heterogeneous structure of embedded System-on-Chip devices communicating on a variety of linked network technologies.This work presents a novel combined hardware and network modelling tool that address the following areas: 1. Development of a system level model of a HomePlug power-line based network, including the fundamental network protocols, the SoC hardware and the physical channel. 2. Use the developed model to explore various system scenarios. 3. Development of alternative hardware algorithms within the design. The model developed uses a Discrete Event simulation method to allow designers to explore areas such as: 1. How does the networking hardware (i.e. the components on the SoC) interact, and what are the issues of changing the algorithms. 2. I low do the nodes on the network interact, as the traffic patterns are different to those found on traditional (office-based) networks, as there will be a greater amount of streaming media

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems